Sentiment Analysis of Tweets on Online Education during COVID-19
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34111-3_21
Reference20 articles.
1. Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., Shah, Z.: Top concerns of Tweeters during the COVID-19 pandemic: infoveillance study. J. Med. Internet Res. 22(4), 19016 (2020). https://doi.org/10.2196/19016
2. Ayaz, T.B., et al.: Global impact of the pandemic on education: a study of natural language processing. In: 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–4. IEEE (2022)
3. Bilen, B., Horasan, F.: LSTM network based sentiment analysis for customer reviews. Politeknik Dergisi 25, 959–966 (2021). https://doi.org/10.2339/politeknik.844019
4. Chintalapudi, N., Battineni, G., Amenta, F.: Sentimental analysis of COVID-19 tweets using deep learning models. Infect. Dis. Rep. 13(2), 329–339 (2021)
5. Coletta, L.F.S., da Silva, N.F.F., Hruschka, E.R., Hruschka, E.R.: Combining classification and clustering for tweet sentiment analysis. In: 2014 Brazilian Conference on Intelligent Systems, pp. 210–215 (2014). https://doi.org/10.1109/BRACIS.2014.46
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